Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI‘s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative AI ideas on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses support learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its reinforcement learning (RL) step, which was used to fine-tune the model’s responses beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it’s geared up to break down complicated inquiries and reason through them in a detailed way. This assisted thinking process permits the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market’s attention as a flexible text-generation model that can be integrated into various workflows such as agents, logical thinking and data analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most appropriate specialist “clusters.” This technique enables the model to specialize in various problem domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and evaluate models against key security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you’re using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, create a limitation increase demand and reach out to your account team.

Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For wiki.asexuality.org instructions, see Establish consents to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful material, and evaluate designs against essential security criteria. You can execute security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The general flow involves the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent out to the design for inference. After receiving the model’s output, another guardrail check is used. If the output passes this last check, it’s returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, wakewiki.de and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.

The design detail page offers vital details about the design’s abilities, rates structure, and execution standards. You can discover detailed usage instructions, forum.pinoo.com.tr consisting of sample API calls and code snippets for integration. The model supports various text generation tasks, consisting of material production, code generation, and concern answering, using its reinforcement learning optimization and CoT thinking capabilities. The page likewise consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, select Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). 5. For Number of circumstances, get in a number of instances (in between 1-100). 6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile